We address the problem of characterizing breast cancer, which today is done using staging guidelines. Our demo will show different breast cancer staging results that leverage the Whyis semantic nanopublication knowledge graph framework . The system we developed is able to ingest breast cancer characterization guidelines in a semi-automated manner and then use our deductive inferencer to generate new information based on those guidelines as described in our ISWC resource track paper ‘Knowledge Integration for Disease Characterization: A Breast Cancer Example’ . In this paper we demonstrate the versatility of our framework using a synthetic patient profile.
The Center for Health Empowerment by Analytics, Learning, and Semantics (HEALS) is a five-year collaboration between Rensselaer and IBM aimed at researching how the application of advanced cognitive computing capabilities can help people to understand and improve their own health conditions.